- Rain: Total rain each year
bins = 9
Year = 1041
v = 'Rain'
p <-
df %>%
select(var, eval(v), Year, var) %>%
rename(region = var,
value = eval(v)) %>%
group_by(Year, region) %>%
#summarise(value = mean(value, na.rm = TRUE), .groups = 'drop') %>%
summarise(value = sum(value, na.rm = TRUE), .groups = 'drop') %>%
ggplot(aes(x = Year, y = value, fill = region, color = region)) +
geom_line( ) +
expand_limits(y = 0 ) +
ylab(v) + xlab('year') +
theme_ipsum() +
labs(title = paste('Total ',v,' per year in both regions'))
# Turn it interactive with ggplotly
ggplotly(p)
- Sm:Soil moisture
bins = 9
Year = 1041
v = 'Sm'
plot_density_(df = df, Year = Year, v = v , bins = bins)
## Picking joint bandwidth of 20.6

plot_ts_meteo_(df = df, v = v, scaleLimits = 5)
v = 'Sm'
data_xts <-
df %>%
select(var, eval(v), Year, var,Date ) %>%
mutate(time_index = as.yearmon(Date)) %>% # create a time index for the data
# mutate(Month = month.name[Month]) %>%
rename(region = var,
value = eval(v)) %>%
group_by(Year, region, time_index) %>%
#summarise(value = mean(value, na.rm = TRUE), .groups = 'drop') %>%
summarise(value = median(value, na.rm = TRUE), .groups = 'drop') %>%
spread(key = region, value = value)
ggseasonplot(
ts(data_xts$rdd, start = c(min(data_xts$Year)), frequency = 12),
polar = TRUE,
year.labels = FALSE,
year.labels.left = FALSE
) +
ylab("Soil moisture") +
ggtitle("Polar seasonal plot: Soil moisture - RDD region") +
theme(legend.position = "none")

ggseasonplot(
ts(data_xts$rvv, start = c(min(data_xts$Year)), frequency = 12),
polar = TRUE,
year.labels = FALSE,
year.labels.left = FALSE
) +
ylab("Soil moisture") +
ggtitle("Polar seasonal plot: Soil moisture - RVV region") +
theme(legend.position = "none")
## Warning: Removed 104 rows containing missing values (`geom_line()`).

df_sm <-
df %>%
select(var, eval(v), Year, var, Month) %>%
rename(region = var,
value = eval(v))
df_sm %>%
#mutate(Month = as.yearmon(Date)) %>% # create a time index for the data
group_by(Year, region) %>%
summarise(value = mean(value, na.rm = TRUE), .groups = 'drop') %>%
ggplot(aes(x = Year, y = value, fill = region, color = region)) +
geom_line( ) +
expand_limits(y = 0 ) +
ylab(v) + xlab('year') +
theme_ipsum() +
labs(title = paste('Mean ',v,' per year in both regions')) +
facet_wrap(~region)

# df_sm %>%
# filter(region == 'rdd') %>%
# group_by(Month, region, Year) %>%
# summarise(value = mean(value), .groups = 'drop') %>%
# ggseasonplot(ts(value, start = c(min(Year)),frequency = 12),
# year.labels = TRUE,
# year.labels.left = TRUE) +
# ylab("$ million") +
# ggtitle("Seasonal plot: antidiabetic drug sales")
# Turn it interactive with ggplotly
#ggplotly(p)